using gps to monitor driving and parking habits in winnipeg for phev optimization

Using GPS to Monitor Driving and Parking Habits in Winnipeg for

PHEV Optimization

R.Smith1, D.Capelle1 and D.Blair1

1University of Winnipeg Department of Geography

Introduction

http://www.eeh.ee.ethz.ch/

What is a PHEV?

Introduction

• Power Requirements: Distance, Speed, Acceleration and Duration

• Time available for Battery Recharging

How do you design a PHEV?

Purpose

• Determine the energy demands placed on a PHEV by a typical driver

• Identify the most suitable public locations for recharging PHEVs

• Decrease vehicle emissions & petroleum dependence

Participants

• 100 Drivers from Winnipeg & nearby communities

• One year period

• Recruitment:– Local media– Word-of mouth – First come first served basis

Equipment

• 100 GPS receivers (Otto Driving Companion)– Store 300 hours of data @ one-second intervals– Plug-in to vehicle lighter socket– Transfer data to PC via USB cable

• Accuracy:– Position: 10 metres– Speed: 1 km/h

myottomate.com/checkoutotto.asp

Duty Cycle Analysis

arcx.com/sites/images/Photos/Underground parking lot at Square One.jpg

Vehicle Power Demand – the Duty Cycle

• A representative, 24-hour profile• Duty Cycles can indicate:

– Typical speed and acceleration demands– Hours of the day vehicle is in operation– Number of Trips / Day– Time available for Recharging

• Measured: Pre-determined route, single vehicle • Derived: Multiple vehicles, thousands of trips

over long periods of time

Duty Cycle Construction

• How many Trips / Cycle ?

• What is the trip origin and destination ?

• What hour of the day ?

• How long and far is the trip ?

• Speed and acceleration ?

• What is “Average” or “Typical” ?

Isolating Specific Trips

HOME

WORK

0

5

10

15

20

25

30

35

40

45

0:00

1:00

2:00

3:00

4:00

5:00

6:00

7:00

8:00

9:00

10:00

11:00

12:00

13:00

14:00

15:00

16:00

17:00

18:00

19:00

20:00

21:00

22:00

23:00

Hour of Day

Percentage

0

2

4

6

8

10

12

14

16

18

20

Sun Mon Tue Wed Thu Fri Sat

Weekday

Percentage

HOME to WORK

0

10

20

30

40

50

60

70

80

90

10:00:05PM

10:01:39PM

10:02:39PM

10:03:39PM

10:04:39PM

10:05:39PM

10:06:51PM

10:07:54PM

Speed (km/h)

CONGESTED FLOW

IDLE

IDLE

CREEP

UN-CONGESTED FLOW

Simplifying Trips

Creating “Blueprints”

0

10

20

30

40

50

60

MTT-1 MTT-2 MTT-3 MTT-4 MTT-5 MTT-6 MTT-7 MTT-8

Micro-Trip Type (MTT)

% of total micro-trips

Idling Micro-trips

Un-congested Traffic Flow

Creep

Congested Traffic Flow

%

Micro-Trip Types

Reconstructing Trips

0

10

20

30

40

50

60

70

80

8:00:00 8:01:30 8:03:00 8:04:30 8:06:00 8:07:30 8:09:00 8:10:30 8:12:00

Speed (km/h)

0

10

20

30

40

50

60

70

80

12:00:00 12:01:00 12:02:00 12:03:00 12:04:00 12:05:00 12:06:00

Speed (km/h)

0

10

20

30

40

50

60

70

80

13:00:00 13:01:00 13:02:00 13:03:00 13:04:00 13:05:00 13:06:00 13:07:00 0

10

20

30

40

50

60

70

80

16:00:00 16:01:30 16:03:00 16:04:30 16:06:00 16:07:30 16:09:00 16:10:30

Speed (km/h)

0

10

20

30

40

50

60

70

80

17:00:00 17:02:00 17:04:00 17:06:00 17:08:00 17:10:00 17:12:00

Speed (km/h)

0

10

20

30

40

50

60

70

80

8:00:00 8:01:30 8:03:00 8:04:30 8:06:00 8:07:30 8:09:00 8:10:30 8:12:00

0

10

20

30

40

50

60

70

80

8:00:00 8:01:30 8:03:00 8:04:30 8:06:00 8:07:30 8:09:00 8:10:30 8:12:00

0

10

20

30

40

50

60

70

80

17:00:00 17:02:00 17:04:00 17:06:00 17:08:00 17:10:00 17:12:00

0

10

20

30

40

50

60

70

80

16:00:00 16:01:30 16:03:00 16:04:30 16:06:00 16:07:30 16:09:00 16:10:30

0

10

20

30

40

50

60

70

80

13:00:00 13:01:00 13:02:00 13:03:00 13:04:00 13:05:00 13:06:00 13:07:00

0

10

20

30

40

50

60

70

80

12:00:00 12:01:00 12:02:00 12:03:00 12:04:00 12:05:00 12:06:000

10

20

30

40

50

60

70

80

13:00:00 13:01:00 13:02:00 13:03:00 13:04:00 13:05:00 13:06:00 13:07:00

X 100

Reconstructing Trips

0

5

10

15

20

25

30

4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10

Distance (km)

Frequency

0

2

4

6

8

10

12

14

16

18

17 18 19 20 21 22 23 24 25 26 27 28 29

Average Trip Speed (km/h)

Frequency

6.5 km 22 km/h

Distance Average Speed

Duty Cycle Construction

0

10

20

30

40

50

60

70

8:00:00 8:01:00 8:02:00 8:03:00 8:04:00 8:05:00 8:06:00 8:07:00 8:08:00 8:09:00 8:10:00 8:11:00 8:12:00 8:13:00 8:14:00 8:15:00 8:16:00 8:17:00

Time

Speed (km/h)

0

10

20

30

40

50

60

70

80

3:00:00 3:01:00 3:02:00 3:03:00 3:04:00 3:05:00 3:06:00 3:07:00 3:08:00 3:09:00 3:10:00

Time

Speed (km/h)

0

10

20

30

40

50

60

70

80

90

15:30:00 15:31:00 15:32:00 15:33:00 15:34:00 15:35:00 15:36:00 15:37:00 15:38:00 15:39:00 15:40:00 15:41:00

Time

Spee

d (k

m/h)

0

10

20

30

40

50

60

70

16:00:00 16:01:00 16:02:00 16:03:00 16:04:00 16:05:00 16:06:00 16:07:00 16:08:00 16:09:00 16:10:00

Time

Speed (km/h)

0

10

20

30

40

50

60

70

17:00:00 17:01:00 17:02:00 17:03:00 17:04:00 17:05:00 17:06:00 17:07:00 17:08:00 17:09:00 17:10:00 17:11:00

Time

Speed (km/h)

HOME to WORKWORK to SCHOOL

SCHOOL to HOMEHOME to SHOPPING

SHOPPING to HOME

TOTAL DISTANCE = 25.4 km

TOTAL DURATION = 1:02:54

Parking Analysis

arcx.com/sites/images/Photos/Underground parking lot at Square One.jpg

Suitability Criteria

• Maximum public availability– Widely-used parking lots

• Maximum re-charge potential– Long mean parking duration

• Low Impact on Electric Grid– “Off-peak electric demand” parking

Filtering & Manipulation

• Isolate only Trip-ends from data set– Parking locations

• Calculate Duration of all Parking Events

– Time difference between trip-end and next trip-start

• Parking On/Off-peak electric demand

Potentially Suitable Lots: Widely Used Areas

Potentially Suitable Lots:Individual Lot Analysis

78 / 85(0.92)

On-peak/ Off-peak

96 minsmean duration

68# participants

 STATISTICS

0.870.931.201.31.1On-peak/Off-peak

1181207210196Mean Duration (mins)

210206558# Participants

GP-5GP-4GP-3GP-2GP-1STATISTICS

Ranking Parking Lots

Suitability Criteria• Widely-used• Long mean-parking

duration• Low impact on

electric grid

  RANK

Lot A Lot B

Widely Used

1 2

Duration 2 1

Off peak 2 1

SUM 5 4

OVERALLless

desirablemore

desirable

Conclusion

The Good:

• GPS and GIS ideal for identifying suitable locations for PHEV recharge infrastructure

• Applicable to other cities

The Bad:

• Sample size too small

• GPS data errors

Acknowledgments• Soheil Shahidinejad, Department of Engineering, University of Manitoba• Dr. Jeff Babb, Department of Math and Stats, University of Winnipeg• Brad Russell, Department of Geography: Map Library, University of Winnipeg• Centre for Forest Interdisciplinary Research (C-FIR)• Pam Godin, Leif Norman and Laura Redpath• Terry Zdan and Dr. Arne Elias, The Centre for Sustainable Transportation (CST)

Funding and Support